{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    "In this example I will build a classifier for churn prediction using a dataset from telecomm industry. You can find the data set in github in the following links.\n",
    "\n",
    "https://github.com/abulbasar/data/tree/master/Churn%20prediction\n",
    "\n",
    "There are two files \n",
    "- churn-bigml-80.csv training data\n",
    "- churn-bigml-20.csv test data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "from sklearn import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code International plan Voice mail plan  \\\n",
       "0    KS             128        415                 No             Yes   \n",
       "1    OH             107        415                 No             Yes   \n",
       "2    NJ             137        415                 No              No   \n",
       "3    OH              84        408                Yes              No   \n",
       "4    OK              75        415                Yes              No   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "3                       2  False  \n",
       "4                       3  False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"/data/churn-bigml-80.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check number of records, number of columns, types of columns and whether the data contains NULL values.\n",
    "\n",
    "As we see it contains 2665 records, 20 columns, and no null values. There are three catagorical values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2666 entries, 0 to 2665\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   State                   2666 non-null   object \n",
      " 1   Account length          2666 non-null   int64  \n",
      " 2   Area code               2666 non-null   int64  \n",
      " 3   International plan      2666 non-null   object \n",
      " 4   Voice mail plan         2666 non-null   object \n",
      " 5   Number vmail messages   2666 non-null   int64  \n",
      " 6   Total day minutes       2666 non-null   float64\n",
      " 7   Total day calls         2666 non-null   int64  \n",
      " 8   Total day charge        2666 non-null   float64\n",
      " 9   Total eve minutes       2666 non-null   float64\n",
      " 10  Total eve calls         2666 non-null   int64  \n",
      " 11  Total eve charge        2666 non-null   float64\n",
      " 12  Total night minutes     2666 non-null   float64\n",
      " 13  Total night calls       2666 non-null   int64  \n",
      " 14  Total night charge      2666 non-null   float64\n",
      " 15  Total intl minutes      2666 non-null   float64\n",
      " 16  Total intl calls        2666 non-null   int64  \n",
      " 17  Total intl charge       2666 non-null   float64\n",
      " 18  Customer service calls  2666 non-null   int64  \n",
      " 19  Churn                   2666 non-null   bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(3)\n",
      "memory usage: 398.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check distribution of the output class. As it shows it contains 85% records are negative. It gives a sense of desired accuracy - which is closure to 90% or more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2278\n",
       "True      388\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Churn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.854464\n",
       "True     0.145536\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Churn.value_counts()/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Account length', 'Area code', 'International plan',\n",
       "       'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n",
       "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
       "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
       "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
       "       'Total intl calls', 'Total intl charge', 'Customer service calls',\n",
       "       'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loaded the test data and performed similar analysis as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 667 entries, 0 to 666\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   State                   667 non-null    object \n",
      " 1   Account length          667 non-null    int64  \n",
      " 2   Area code               667 non-null    int64  \n",
      " 3   International plan      667 non-null    object \n",
      " 4   Voice mail plan         667 non-null    object \n",
      " 5   Number vmail messages   667 non-null    int64  \n",
      " 6   Total day minutes       667 non-null    float64\n",
      " 7   Total day calls         667 non-null    int64  \n",
      " 8   Total day charge        667 non-null    float64\n",
      " 9   Total eve minutes       667 non-null    float64\n",
      " 10  Total eve calls         667 non-null    int64  \n",
      " 11  Total eve charge        667 non-null    float64\n",
      " 12  Total night minutes     667 non-null    float64\n",
      " 13  Total night calls       667 non-null    int64  \n",
      " 14  Total night charge      667 non-null    float64\n",
      " 15  Total intl minutes      667 non-null    float64\n",
      " 16  Total intl calls        667 non-null    int64  \n",
      " 17  Total intl charge       667 non-null    float64\n",
      " 18  Customer service calls  667 non-null    int64  \n",
      " 19  Churn                   667 non-null    bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(3)\n",
      "memory usage: 99.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"/data/churn-bigml-20.csv\")\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.857571\n",
       "True     0.142429\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Churn.value_counts()/len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2501875468867217"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_test)/len(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort out of categorical and numeric columns so that it can be passed to pipeline for pre-proceessing steps. In the processing steps, we are doing the following \n",
    "- replace any missing numeric values with column median\n",
    "- perform standard scaling for numeric values\n",
    "- one hot encode the categorical columns\n",
    "\n",
    "\n",
    "Althought the Area Code is numeric, here I am considering this as categorical since it is a qualitative variable in nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_columns = ['State', 'Area code', 'International plan', 'Voice mail plan']\n",
    "num_columns = ['Account length', 'Number vmail messages', 'Total day minutes',\n",
    "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
    "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
    "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
    "       'Total intl calls', 'Total intl charge', 'Customer service calls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"Churn\"\n",
    "X_train = df_train.drop(columns=target)\n",
    "y_train = df_train[target]\n",
    "X_test = df_test.drop(columns=target)\n",
    "y_test = df_test[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipe = pipeline.Pipeline([\n",
    "    ('imputer', impute.SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "    ('onehot', preprocessing.OneHotEncoder(handle_unknown='error', drop=\"first\"))\n",
    "]) \n",
    "\n",
    "num_pipe = pipeline.Pipeline([\n",
    "    ('imputer', impute.SimpleImputer(strategy='median')),\n",
    "    ('scaler', preprocessing.StandardScaler()),\n",
    "])\n",
    "\n",
    "preprocessing_pipe = compose.ColumnTransformer([\n",
    "    (\"cat\", cat_pipe, cat_columns),\n",
    "    (\"num\", num_pipe, num_columns)\n",
    "])\n",
    "\n",
    "X_train = preprocessing_pipe.fit_transform(X_train)\n",
    "X_test = preprocessing_pipe.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a basic logistic regression model and check the accuracy. Basic logistic regression model gives accuracy of 85%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8545727136431784"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = linear_model.LogisticRegression(solver=\"liblinear\")\n",
    "lr.fit(X_train, y_train)\n",
    "y_test_pred = lr.predict(X_test)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print classification report. The report shows that precision and recall score quite poor. Accuracy is 85%. Confusion metrics shows a high number of false positive and false negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.88      0.96      0.92       572\n",
      "        True       0.48      0.22      0.30        95\n",
      "\n",
      "    accuracy                           0.85       667\n",
      "   macro avg       0.68      0.59      0.61       667\n",
      "weighted avg       0.82      0.85      0.83       667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[549,  23],\n",
       "       [ 74,  21]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we build a similar model using XGBoost. Performance the model is slightly better than logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRFClassifier(alpha=10, base_score=0.5, colsample_bylevel=1,\n",
      "                colsample_bynode=0.8, colsample_bytree=0.3, gamma=0,\n",
      "                learning_rate=0.5, max_delta_step=0, max_depth=4,\n",
      "                min_child_weight=1, missing=None, n_estimators=25, n_jobs=1,\n",
      "                nthread=None, objective='binary:logistic', random_state=0,\n",
      "                reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=1,\n",
      "                silent=None, subsample=0.8, verbosity=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8800599700149925"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls = xgb.XGBRFClassifier(max_depth=4\n",
    "                          , objective='binary:logistic'\n",
    "                          , alpha = 10\n",
    "                          , n_estimators = 25\n",
    "                          , learning_rate = 0.5\n",
    "                          , colsample_bytree = 0.3\n",
    "                          , seed=1\n",
    "                         )\n",
    "print(cls.fit(X_train, y_train))\n",
    "cls.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = cls.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[571,   1],\n",
       "       [ 79,  16]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31099036, 0.40282923, 0.511349  , 0.3027082 , 0.314027  ,\n",
       "       0.31331146, 0.29674894, 0.40983036, 0.30278635, 0.30884126,\n",
       "       0.3084255 , 0.3237787 , 0.29980737, 0.29623997, 0.3001956 ,\n",
       "       0.30677882, 0.3541156 , 0.30923405, 0.30700096, 0.41554406,\n",
       "       0.30763885, 0.30851945, 0.3265498 , 0.30329952, 0.33966702,\n",
       "       0.3001956 , 0.30962446, 0.29595733, 0.32546172, 0.33571705,\n",
       "       0.29776978, 0.30850208, 0.34021458, 0.30725682, 0.45328048,\n",
       "       0.4982425 , 0.3680964 , 0.32808566, 0.3183908 , 0.30673152,\n",
       "       0.29743543, 0.41480193, 0.3116183 , 0.3553783 , 0.30557564,\n",
       "       0.30309403, 0.37942532, 0.37474954, 0.31575185, 0.3599037 ,\n",
       "       0.2996326 , 0.30419403, 0.30831409, 0.30696604, 0.3001956 ,\n",
       "       0.316106  , 0.52427864, 0.32576826, 0.37717843, 0.30278635,\n",
       "       0.47581154, 0.32551965, 0.50642335, 0.30231136, 0.37139198,\n",
       "       0.43843788, 0.36695373, 0.30839115, 0.33207554, 0.30481374,\n",
       "       0.2955656 , 0.3012349 , 0.313019  , 0.30700096, 0.38549197,\n",
       "       0.30582166, 0.30700096, 0.37549537, 0.3430249 , 0.29822645,\n",
       "       0.34280875, 0.30498305, 0.31311306, 0.3105507 , 0.37323287,\n",
       "       0.3105507 , 0.31416157, 0.3050773 , 0.31417575, 0.3116883 ,\n",
       "       0.2996326 , 0.30044758, 0.31365088, 0.3100524 , 0.35565698,\n",
       "       0.30796114, 0.41056934, 0.30773765, 0.37125212, 0.30231136,\n",
       "       0.3182892 , 0.29595733, 0.32214403, 0.30084613, 0.514858  ,\n",
       "       0.3001956 , 0.30305502, 0.32529062, 0.3006697 , 0.4945866 ,\n",
       "       0.40740535, 0.33151445, 0.31766897, 0.3006697 , 0.3655233 ,\n",
       "       0.43429205, 0.30672383, 0.32205132, 0.30070624, 0.3085198 ,\n",
       "       0.29764232, 0.3109793 , 0.3257239 , 0.4013635 , 0.3359686 ,\n",
       "       0.33943328, 0.2967487 , 0.30771843, 0.30498305, 0.3040563 ,\n",
       "       0.3393332 , 0.3050479 , 0.38714585, 0.29852712, 0.45280787,\n",
       "       0.38155106, 0.3029593 , 0.45380425, 0.3012039 , 0.3029398 ,\n",
       "       0.34105185, 0.30728188, 0.30531117, 0.31670114, 0.3105507 ,\n",
       "       0.32177046, 0.4573966 , 0.3181568 , 0.30152598, 0.3909675 ,\n",
       "       0.31294426, 0.3085198 , 0.33400023, 0.32077488, 0.47786012,\n",
       "       0.40639204, 0.33050343, 0.3319615 , 0.32344657, 0.33500922,\n",
       "       0.3027082 , 0.40056598, 0.307445  , 0.43088666, 0.30789775,\n",
       "       0.33421826, 0.33107135, 0.37163845, 0.40500087, 0.3321316 ,\n",
       "       0.41735464, 0.30481374, 0.30231136, 0.31236097, 0.3105507 ,\n",
       "       0.5064471 , 0.30261776, 0.4047789 , 0.30231136, 0.3405335 ,\n",
       "       0.2950372 , 0.31007975, 0.3087536 , 0.3269005 , 0.42504847,\n",
       "       0.31598032, 0.3685909 , 0.2950372 , 0.32455167, 0.34009004,\n",
       "       0.30700096, 0.31417575, 0.33052325, 0.31045046, 0.29831356,\n",
       "       0.41438964, 0.29796633, 0.30663314, 0.29920763, 0.3029086 ,\n",
       "       0.3063299 , 0.31539193, 0.3116883 , 0.5079945 , 0.3001956 ,\n",
       "       0.2991277 , 0.3041121 , 0.30109063, 0.32607472, 0.37447107,\n",
       "       0.30498305, 0.30777144, 0.30100238, 0.37876102, 0.31608605,\n",
       "       0.30588385, 0.3082546 , 0.3826888 , 0.37298068, 0.30730093,\n",
       "       0.30305377, 0.29759827, 0.30481374, 0.3040563 , 0.30231136,\n",
       "       0.40851396, 0.4965086 , 0.3082546 , 0.3326467 , 0.33788976,\n",
       "       0.32542333, 0.3341007 , 0.2955656 , 0.31771234, 0.3149408 ,\n",
       "       0.31416157, 0.3326467 , 0.30318677, 0.4037371 , 0.31045046,\n",
       "       0.32420975, 0.32247955, 0.2950372 , 0.31436512, 0.30387005,\n",
       "       0.30070624, 0.33540103, 0.3105507 , 0.33772472, 0.31852424,\n",
       "       0.32314363, 0.31257227, 0.38138014, 0.31430024, 0.30231136,\n",
       "       0.29465535, 0.3334996 , 0.30056408, 0.30175948, 0.35587937,\n",
       "       0.30481374, 0.2955656 , 0.30660778, 0.30989522, 0.36803985,\n",
       "       0.30070624, 0.30742687, 0.30404764, 0.33267295, 0.36235425,\n",
       "       0.31819725, 0.31380707, 0.2960253 , 0.3045717 , 0.3384334 ,\n",
       "       0.30481374, 0.37975127, 0.30076206, 0.3105507 , 0.48167092,\n",
       "       0.32600212, 0.29595733, 0.3027082 , 0.3085198 , 0.29693243,\n",
       "       0.34304506, 0.3061247 , 0.44589067, 0.30070624, 0.36165044,\n",
       "       0.32669365, 0.3085198 , 0.3027082 , 0.31294128, 0.32600212,\n",
       "       0.33619794, 0.29595733, 0.3085198 , 0.38301393, 0.41043988,\n",
       "       0.32491407, 0.29920763, 0.2992771 , 0.3177704 , 0.3006697 ,\n",
       "       0.33663702, 0.31560937, 0.3097644 , 0.30688974, 0.29868558,\n",
       "       0.3085198 , 0.3018856 , 0.39641908, 0.32698622, 0.32355228,\n",
       "       0.30611995, 0.3107964 , 0.31213194, 0.2981481 , 0.3270582 ,\n",
       "       0.31737193, 0.31694692, 0.3085198 , 0.3119837 , 0.31558368,\n",
       "       0.2955656 , 0.30529323, 0.5152369 , 0.30688974, 0.2992771 ,\n",
       "       0.30318895, 0.31612805, 0.32925102, 0.30588385, 0.30925852,\n",
       "       0.2955656 , 0.30498305, 0.40822756, 0.30851945, 0.2992771 ,\n",
       "       0.4398431 , 0.31271228, 0.38708156, 0.3085198 , 0.31045046,\n",
       "       0.35840744, 0.3082546 , 0.30150488, 0.32175225, 0.4652535 ,\n",
       "       0.2950372 , 0.5097772 , 0.30102673, 0.3116883 , 0.35951766,\n",
       "       0.41300648, 0.33427966, 0.31117168, 0.30261776, 0.31516036,\n",
       "       0.32941782, 0.30498305, 0.47446254, 0.36190107, 0.36007208,\n",
       "       0.30498305, 0.29595733, 0.3012039 , 0.3085198 , 0.3074227 ,\n",
       "       0.30663314, 0.3084255 , 0.30802375, 0.32086053, 0.30261776,\n",
       "       0.33756632, 0.3082546 , 0.33156782, 0.30217254, 0.29639804,\n",
       "       0.30753097, 0.3141817 , 0.31797275, 0.33216366, 0.3310415 ,\n",
       "       0.49491295, 0.2955656 , 0.3183908 , 0.33494854, 0.29939222,\n",
       "       0.30328164, 0.33852148, 0.2954584 , 0.30759698, 0.32841137,\n",
       "       0.3001956 , 0.3240351 , 0.31607246, 0.41369408, 0.32458228,\n",
       "       0.36818975, 0.4840899 , 0.33752128, 0.37297237, 0.3001956 ,\n",
       "       0.30498305, 0.3001956 , 0.29759827, 0.3085198 , 0.39438868,\n",
       "       0.3105507 , 0.32176316, 0.30730093, 0.2955656 , 0.30162525,\n",
       "       0.3372115 , 0.3243551 , 0.3105507 , 0.451918  , 0.33685973,\n",
       "       0.49760103, 0.2974328 , 0.35465008, 0.3790691 , 0.37022647,\n",
       "       0.4675231 , 0.30042017, 0.30278635, 0.3284777 , 0.29980737,\n",
       "       0.37387934, 0.30607077, 0.3085198 , 0.34019262, 0.33480656,\n",
       "       0.30531117, 0.33151445, 0.43393907, 0.2929215 , 0.41985604,\n",
       "       0.34022316, 0.34111473, 0.517678  , 0.31373057, 0.40940163,\n",
       "       0.31770596, 0.31612805, 0.3104943 , 0.42046744, 0.3311268 ,\n",
       "       0.34814557, 0.31311306, 0.30070624, 0.31353185, 0.30498305,\n",
       "       0.43508592, 0.36923683, 0.4763085 , 0.32600212, 0.3085198 ,\n",
       "       0.38423043, 0.4733591 , 0.30652794, 0.3650237 , 0.3457665 ,\n",
       "       0.3118075 , 0.30481374, 0.2929215 , 0.41787073, 0.3001956 ,\n",
       "       0.29595733, 0.32830444, 0.3105507 , 0.34526575, 0.3132043 ,\n",
       "       0.29595733, 0.29465535, 0.37231737, 0.32433093, 0.3027336 ,\n",
       "       0.3029593 , 0.31490865, 0.30459133, 0.35326818, 0.32427636,\n",
       "       0.30679718, 0.31215116, 0.30070624, 0.3085198 , 0.3027082 ,\n",
       "       0.3006697 , 0.32910725, 0.39973086, 0.30498305, 0.30607077,\n",
       "       0.38208032, 0.3085198 , 0.3001956 , 0.3003109 , 0.33376795,\n",
       "       0.39263165, 0.30855724, 0.3105507 , 0.31311306, 0.33803454,\n",
       "       0.37200576, 0.34231952, 0.330997  , 0.30700096, 0.39911482,\n",
       "       0.36621624, 0.31827876, 0.30186337, 0.29867545, 0.3356101 ,\n",
       "       0.3085198 , 0.31145316, 0.31248832, 0.3027082 , 0.31406873,\n",
       "       0.35201207, 0.30481374, 0.2992771 , 0.30513301, 0.37308925,\n",
       "       0.36794993, 0.30110082, 0.3127121 , 0.33322498, 0.4449158 ,\n",
       "       0.31168917, 0.30673152, 0.30498305, 0.30700096, 0.3068707 ,\n",
       "       0.3091831 , 0.30512092, 0.3027082 , 0.30660778, 0.30625892,\n",
       "       0.3271928 , 0.3063089 , 0.35488492, 0.3449039 , 0.30359235,\n",
       "       0.3018856 , 0.36643347, 0.35050032, 0.34450686, 0.5095099 ,\n",
       "       0.32542938, 0.30485725, 0.5035671 , 0.340484  , 0.33671978,\n",
       "       0.30728188, 0.30989522, 0.31045046, 0.34544092, 0.33663702,\n",
       "       0.30070624, 0.5298415 , 0.30087772, 0.3109793 , 0.3068707 ,\n",
       "       0.326024  , 0.2950372 , 0.3252702 , 0.3372208 , 0.3027082 ,\n",
       "       0.3048846 , 0.3197322 , 0.5109767 , 0.51692885, 0.30841428,\n",
       "       0.3380597 , 0.31260023, 0.29867545, 0.3668453 , 0.37275618,\n",
       "       0.30498305, 0.3105507 , 0.409105  , 0.2955656 , 0.30305377,\n",
       "       0.29867545, 0.314027  , 0.30700096, 0.30481374, 0.3001956 ,\n",
       "       0.4383142 , 0.3342078 , 0.30350983, 0.36209542, 0.32651186,\n",
       "       0.320986  , 0.3205879 , 0.3235914 , 0.30109063, 0.3082075 ,\n",
       "       0.31722125, 0.3383163 , 0.31311306, 0.31126875, 0.30286813,\n",
       "       0.3802425 , 0.5061233 , 0.3001956 , 0.2980996 , 0.30700096,\n",
       "       0.3006697 , 0.31847972, 0.32148975, 0.30498305, 0.34615207,\n",
       "       0.4101764 , 0.3052203 , 0.30784267, 0.44082254, 0.3729254 ,\n",
       "       0.3045799 , 0.30796114, 0.30531117, 0.31417575, 0.35196403,\n",
       "       0.3291643 , 0.29764232, 0.3217549 , 0.30286813, 0.32958367,\n",
       "       0.30739668, 0.30911547, 0.32656053, 0.30070624, 0.342777  ,\n",
       "       0.3542476 , 0.45026606, 0.3243551 , 0.3085198 , 0.33032906,\n",
       "       0.316153  , 0.35106146, 0.3001956 , 0.3001956 , 0.2955656 ,\n",
       "       0.3257471 , 0.30070624, 0.30470696, 0.32815665, 0.29939222,\n",
       "       0.35628033, 0.29867545, 0.53154457, 0.51904196, 0.30591944,\n",
       "       0.33938977, 0.3650237 , 0.3068482 , 0.30070624, 0.31722125,\n",
       "       0.29595733, 0.4850426 , 0.2996326 , 0.30474317, 0.30519122,\n",
       "       0.30498305, 0.33452788], dtype=float32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_prob = cls.predict_proba(X_test)[:, 1]\n",
    "y_test_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9060820758189179"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc = metrics.roc_auc_score(y_test, y_test_prob)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftr, tpr, thresholds = metrics.roc_curve(y_test, y_test_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC, auc: 0.9060820758189179')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 5,5\n",
    "plt.plot(ftr, tpr)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC, auc: \" + str(auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost cross validation parameters\n",
    "\n",
    "- num_boost_round: denotes the number of trees you build (analogous to n_estimators)\n",
    "- metrics: tells the evaluation metrics to be watched during CV\n",
    "- as_pandas: to return the results in a pandas DataFrame.\n",
    "- early_stopping_rounds: finishes training of the model early if the hold-out metric (\"rmse\" in our case) does not improve for a given number of rounds.\n",
    "- seed: for reproducibility of results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train-error-mean</th>\n",
       "      <th>train-error-std</th>\n",
       "      <th>test-error-mean</th>\n",
       "      <th>test-error-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.142629</td>\n",
       "      <td>0.004251</td>\n",
       "      <td>0.146658</td>\n",
       "      <td>0.009542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.138973</td>\n",
       "      <td>0.008705</td>\n",
       "      <td>0.143281</td>\n",
       "      <td>0.011960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.134566</td>\n",
       "      <td>0.009634</td>\n",
       "      <td>0.138779</td>\n",
       "      <td>0.018482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-error-mean  train-error-std  test-error-mean  test-error-std\n",
       "0          0.142629         0.004251         0.146658        0.009542\n",
       "1          0.138973         0.008705         0.143281        0.011960\n",
       "2          0.134566         0.009634         0.138779        0.018482"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\"objective\": \"binary:logistic\"\n",
    "          , 'colsample_bytree': 0.3\n",
    "          , 'learning_rate': 0.1\n",
    "          , 'max_depth': 5\n",
    "          , 'alpha': 10\n",
    "         }\n",
    "\n",
    "data_dmatrix = xgb.DMatrix(data=X_train,label=y_train) \n",
    "\n",
    "cv_results = xgb.cv(dtrain=data_dmatrix\n",
    "                    , params=params\n",
    "                    , nfold=5\n",
    "                    , num_boost_round=200\n",
    "                    , early_stopping_rounds=10\n",
    "                    , metrics=\"error\"\n",
    "                    , as_pandas=True\n",
    "                    , seed=123)\n",
    "\n",
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a268880d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install graphviz to display the decision graph\n",
    "\n",
    "```\n",
    "$ conda install graphviz python-graphviz\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2816b150>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 20,20\n",
    "\n",
    "xgb.plot_tree(cls, num_trees=0, rankdir='LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots provide insight into how the model arrived at its final decisions and what splits it made to arrive at those decisions.\n",
    "\n",
    "Note that if the above plot throws the 'graphviz' error on your system, consider installing the graphviz package via pip install graphviz on cmd. You may also need to run sudo apt-get install graphviz on cmd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a256e4fd0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] =15, 15\n",
    "xgb.plot_importance(cls, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 1.03670331e-02, 4.19097766e-03, 3.83628905e-03,\n",
       "       0.00000000e+00, 1.47102531e-02, 0.00000000e+00, 6.88518956e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 9.52065457e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.06063336e-02, 1.16738016e-02,\n",
       "       5.95240993e-03, 0.00000000e+00, 7.51474779e-03, 0.00000000e+00,\n",
       "       7.38185551e-03, 4.19134647e-03, 1.50282597e-02, 0.00000000e+00,\n",
       "       0.00000000e+00, 5.80903981e-03, 2.30110739e-03, 1.06669767e-02,\n",
       "       1.00182148e-03, 8.28746613e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.30601572e-02, 1.20007638e-02, 0.00000000e+00, 7.65800802e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 6.78446749e-03, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 6.39448082e-03, 1.27225678e-04,\n",
       "       0.00000000e+00, 5.56971179e-03, 1.45709030e-02, 1.01845795e-02,\n",
       "       2.11426437e-01, 2.95618586e-02, 8.65735114e-03, 4.65510413e-02,\n",
       "       6.36982545e-02, 1.12765599e-02, 5.84137216e-02, 1.98860224e-02,\n",
       "       9.24697518e-03, 3.31619754e-02, 1.65987909e-02, 1.11362496e-02,\n",
       "       1.38384867e-02, 3.55013832e-02, 3.33783738e-02, 2.21542772e-02,\n",
       "       1.59236327e-01], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(categories='auto', drop='first', dtype=<class 'numpy.float64'>,\n",
       "              handle_unknown='error', sparse=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder = preprocessing_pipe.transformers_[0][1].steps[1][1]\n",
    "one_hot_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_AL', 'x0_AR', 'x0_AZ', 'x0_CA', 'x0_CO', 'x0_CT', 'x0_DC',\n",
       "       'x0_DE', 'x0_FL', 'x0_GA', 'x0_HI', 'x0_IA', 'x0_ID', 'x0_IL',\n",
       "       'x0_IN', 'x0_KS', 'x0_KY', 'x0_LA', 'x0_MA', 'x0_MD', 'x0_ME',\n",
       "       'x0_MI', 'x0_MN', 'x0_MO', 'x0_MS', 'x0_MT', 'x0_NC', 'x0_ND',\n",
       "       'x0_NE', 'x0_NH', 'x0_NJ', 'x0_NM', 'x0_NV', 'x0_NY', 'x0_OH',\n",
       "       'x0_OK', 'x0_OR', 'x0_PA', 'x0_RI', 'x0_SC', 'x0_SD', 'x0_TN',\n",
       "       'x0_TX', 'x0_UT', 'x0_VA', 'x0_VT', 'x0_WA', 'x0_WI', 'x0_WV',\n",
       "       'x0_WY', 'x1_415', 'x1_510', 'x2_Yes', 'x3_Yes'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('imputer',\n",
       "                 SimpleImputer(add_indicator=False, copy=True,\n",
       "                               fill_value='missing', missing_values=nan,\n",
       "                               strategy='constant', verbose=0)),\n",
       "                ('onehot',\n",
       "                 OneHotEncoder(categories='auto', drop='first',\n",
       "                               dtype=<class 'numpy.float64'>,\n",
       "                               handle_unknown='error', sparse=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_pipe.transformers_[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
